The present invention relates to mobile telecommunication systems, and more particularly to methods and apparatuses that determine high velocity relative movement between a transmitter and a receiver in a telecommunication system.
Digital communication systems include time-division multiple access (TDMA) systems, such as cellular radio telephone systems that comply with the GSM telecommunication standard and its enhancements like GSM/EDGE, and Code-Division Multiple Access (CDMA) systems, such as cellular radio telephone systems that comply with the IS-95, cdma2000, and Wideband CDMA (WCDMA) telecommunication standards. Digital communication systems also include “blended” TDMA and CDMA systems, such as cellular radio telephone systems that comply with the Universal Mobile Telecommunications System (UMTS) standard, which specifies a third generation (3G) mobile system being developed by the European Telecommunications Standards Institute (ETSI) within the International Telecommunication Union's (ITU's) IMT-2000 framework. The Third Generation Partnership Project (3GPP) promulgates the UMTS standard. This application focuses on WCDMA systems for economy of explanation, but it will be understood that the principles described in this application can be implemented in other digital communication systems.
WCDMA is based on direct-sequence spread-spectrum techniques, with pseudo-noise scrambling codes and orthogonal channelization codes separating base stations and physical channels (user equipment or users), respectively, in the downlink (base-to-user equipment) direction. User Equipment (UE) communicates with the system through, for example, respective dedicated physical channels (DPCHs). WCDMA terminology is used here, but it will be appreciated that other systems have corresponding terminology. Scrambling and channelization codes and transmit power control are well known in the art.
FIG. 1 depicts a mobile radio cellular telecommunication system 100, which may be, for example, a CDMA or a WCDMA communication system. Radio network controllers (RNCs) 112, 114 control various radio network functions including for example radio access bearer setup, diversity handover, and the like. More generally, each RNC directs UE calls via the appropriate base station(s) (BSs). The UE and BS communicate with each other through downlink (i.e., base-to-UE or forward) and uplink (i.e., UE-to-base or reverse) channels. RNC 112 is shown coupled to BSs 116, 118, 120, and RNC 114 is shown coupled to BSs 122, 124, 126. Each BS serves a geographical area that can be divided into one or more cell(s). BS 126 is shown as having five antenna sectors S1-S5, which can be said to make up the cell of the BS 126. The BSs are coupled to their corresponding RNCs by dedicated telephone lines, optical fiber links, microwave links, and the like. Both RNCs 112, 114 are connected with external networks such as the public switched telephone network (PSTN), the Internet, and the like through one or more core network nodes like a mobile switching center (not shown) and/or a packet radio service node (not shown). In FIG. 1, UE 128 is shown communicating with BS 118. UE 130 is shown communicating with plural base stations, namely BSs 120 and 122. A control link between RNCs 112, 114 permits diversity communications to/from UE 130 via BSs 120, 122.
At the UE, the modulated carrier signal (Layer 1) is processed to produce an estimate of the original information data stream intended for the receiver. The composite received baseband spread signal is commonly provided to a RAKE processor that includes a number of “fingers”, or de-spreaders, that are each assigned to respective ones of selected components, such as multipath echoes or streams from different base stations, in the received signal. Each finger combines a received component with the scrambling sequence and the appropriate channelization code so as to de-spread a component of the received composite signal. The RAKE processor typically de-spreads both sent information data and pilot or training symbols that are included in the composite signal.
In a typical wireless communication system, each device (e.g. UE, BS) has its own local oscillator which defines a time reference. It is crucial that the local oscillators of devices communicating with each other be aligned as precisely as possible, otherwise their time references will drift in relation to each other. This drift could lead to the devices no longer being capable of receiving information properly from each other, which in turn causes degraded receiver performance. Ultimately, the connection may be lost due to loss of synchronization between the UE and BS.
This applies in particular to wireless telecommunication systems such as WCDMA. In such systems, the UE applies an automatic frequency control (AFC) mechanism to adjust its local oscillator in a manner that keeps it well aligned with the local oscillators of the base station(s) it is connected to.
Typical operation of the AFC comprises analyzing a characteristic (e.g., complex channel estimates) over time, and attempting to adjust the local oscillator such that no rotation of the channel estimates are detected in the complex plane. This algorithm is based on the fact that rotation corresponds to relative frequency drift, which in turn corresponds to relative time reference drift.
FIG. 2 is a block diagram of the parts of a UE involved in AFC operation. Of particular relevance to this discussion is the local oscillator (VCXO) 201 which generates the frequencies necessary for operating the Front End Receiver (RX Fe) 203 and Front End Transmitter (not shown) sections. An AFC 205 generates a digital control signal (ferr) that, after conversion to an analog control voltage by a Digital-to-Analog Converter (DAC) 207 adjusts the output frequency of the local oscillator 201.
The AFC 205 may be selectively operated in one of a plurality of different speed modes. The speed mode may be set by a Doppler estimator 209.
Consider an example in which there are two different speed modes. In an exemplary low speed mode, one channel estimate per finger is collected in each slot, and in an exemplary high speed mode five channel estimates per finger are collected in each slot. The value
                    y        =                              ∑            f                    ⁢                                                                      h                  ^                                f                            ⁡                              (                                                      h                    ^                                    f                                      (                    previous                    )                                                  )                                      *                                              (        1        )            (where “*” denotes complex conjugation) is calculated and then filtered according toyfilt=λ(y−yfilt(previous))+yfilt(previous)  (2)where λ is a filter parameter, f denotes the fingers involved in AFC operation, and ĥf and ĥf(previous) are the current and previous channel estimates, respectively, for finger f, each generated by a channel estimator 211. The filter state is appropriately reset whenever an update of the UE frequency reference (fUE) or a speedmode change occurs. The reported frequency error ferr is calculated as
                              φ          =                      arg            ⁡                          (                              y                filt                            )                                      ,                              f            err                    =                      φ                          2              ⁢                                                          ⁢              π              ⁢                                                          ⁢              Δ              ⁢                                                          ⁢              t                                      ,                            (        3        )            where Δt is the time interval between two consecutive updates of the AFC (i.e., two consecutive collected channel estimates), for example 1/1500 seconds in low speed mode and 1/7500 seconds in high speed mode.
In the arrangement as described above, there is a high risk of AFC wrap-around in certain situations. The wrap-around occurs when
                                                    Δ            ⁢                                                  ⁢            f                                    >                  1                      2            ⁢                                                  ⁢            Δ            ⁢                                                  ⁢            t                                              (        4        )            where Δf is the frequency error caused by the Doppler shift together with the difference between the BS transmit frequency reference and the UE receive frequency reference, and Δt is the time interval between two consecutive updates of the AFC (i.e., two consecutive collected channel estimates). The inequality expressed in Equation (4) corresponds to a situation in which the channel estimates rotate more than ±π between two consecutive channel estimates collected by the AFC, which results in the frequency error ferr reported by the AFC being erroneous by a multiple of
      1          Δ      ⁢                          ⁢      t        ⁢          ⁢      Hz    .  As an example, a UE can be designed in which the AFC is updated once every slot in low speed mode, whereby
      1          2      ⁢                          ⁢      Δ      ⁢                          ⁢      t        =      750    ⁢                  ⁢          Hz      .      In an exemplary high speed mode, the UE's AFC can be updated five times every slot, whereby
      1          2      ⁢                          ⁢      Δ      ⁢                          ⁢      t        =      3750    ⁢                  ⁢          Hz      .      It can be seen that the AFC is substantially more tolerant of frequency errors in high speed mode than in low speed mode. It is noted that in other embodiments that call for a different number of channel estimates per slot, different values of Δt are obtained. Further, as mentioned earlier, the number of speed modes may be higher than two.
It should be noted that, in current applications of WCDMA, the UE goes out-of-sync if the correct frequency reference is not restored within approximately 50-150 slots.
A scenario that is especially vulnerable to AFC wrap-around when the UE is moving at high relative velocities (assuming that the AFC is operating in low speed mode) is that in which the UE is passing a base station closely (less than 10 m or so).
In such a scenario, a wrap-around event can occur for relative velocities around and above 185 km/h (i.e., the UE's velocity relative to the base station) for embodiments in which Δt= 1/1500.
FIGS. 3(a)-(c) through 5(a)-(c) are graphs depicting exemplary results obtained by simulating the above-described scenario, employing a one-tap line-of-sight (LOS) channel without fading or interference.
In this simulation, the UE is assumed to pass the base station at a distance of 2 m. The UE frequency reference is shown without the carrier frequency component. The same settings apply to all simulations shown in this specification.
FIGS. 3a-c are graphs depicting the tracking ability of the AFC 205 when it remains in a low speed mode (speedmode equals zero, meaning low speed mode, for all slots as depicted in the graph of FIG. 3c) at a relative velocity of 150 km/h. FIG. 3a allows a comparison to be made between the true Doppler shift (graph 301) and the shift in the UE frequency (graph 303). FIG. 3b allows a comparison to be made between the true frequency error (graph 305) and the reported frequency error (graph 307) generated by the AFC 205. It can be seen that, at this relative velocity, the UE is able to follow the Doppler shift caused frequency change even when the AFC update rate is low.
FIGS. 4a-c are graphs depicting the tracking ability of the AFC 205 when it remains in a low speed mode (speedmode equals zero, meaning low speed mode, for all slots as depicted in the graph of FIG. 4c) but the relative velocity increases to 350 km/h. FIG. 4a allows a comparison to be made between the true Doppler shift (graph 401) and the shift in the UE frequency (graph 403). FIG. 4b allows a comparison to be made between the true frequency error (graph 405) and the reported frequency error (graph 407) generated by the AFC 205. As can be seen, at this relative velocity the AFC 205 is incapable of tracking the Doppler shift caused frequency change when it is updated at the low rate.
FIGS. 5a-c are graphs depicting the tracking ability of the AFC 205 when it operates at a high speed mode (speedmode equals one, meaning high speed mode, for all slots as depicted in the graph of FIG. 5c) and the relative velocity is 350 km/h. FIG. 5a allows a comparison to be made between the true Doppler shift (graph 501) and the shift in the UE frequency (graph 503). FIG. 5b allows a comparison to be made between the true frequency error (graph 505) and the reported frequency error (graph 507) generated by the AFC 205. As can be seen, even at this relative velocity the AFC 205 is able to track the Doppler shift caused frequency change when it is updated at the high rate.
In an exemplary UE, wrap-around occurs when Δf>3750 Hz if the AFC 205 is operating in high speed mode. Hence, wrap-around would occur around and above 935 km/h for the above-described scenario. This indicates that if the AFC were in high speed mode at the proper moments in time, the wrap-around problem would be resolved for all currently realistic velocities. However, it may be undesirable to run the AFC in high speed modes at all times because the AFC may become more sensitive to noise, which can result in unnecessary toggling in UE frequency compensations in high speed mode. This is one reason why a Doppler estimator 209 is employed in the exemplary UE shown in FIG. 2: The Doppler estimator 209 determines the state of speedmode, which in turn governs whether the AFC update rate will be high or low. (The Doppler estimator 209 also may be used for other purposes in the receiver chain, such as setting parameters for, e.g., filters for channel estimation, SIR estimation, and the like; and turning on and off algorithms, e.g., GRAKE in low speed mode and RAKE in high speed mode.) Different Doppler estimation algorithms can be considered for this purpose, such as the level crossing algorithm and the argument (or zero) crossing algorithm. As will be explained, however, both of these algorithms have problems with detecting high speed situations under line-of-sight (LOS) conditions, since the algorithms measure fading properties (which are related to the Doppler spread) and not velocity itself. A Doppler estimate is related to the fading characteristics of the channel, and is assumed to be approximately proportional to the relative velocity of the device, which is true for Rayleigh fading channels, but not at all for channels with little (e.g., Ricean) or no fading (e.g., Additive White Gaussian Noise, or “AWGN”).
The level crossing algorithm counts the number of times the absolute value of, for example, the complex channel estimate or an estimated signal-to-interference ratio (SIR) value crosses a given level, and converts the number of registered level crossings into a Doppler estimate.
The level crossing algorithm is based on the assumption that the higher the relative velocity is, the faster the fading is, and hence the number of level crossings per time unit should correspond to the relative velocity. This is a quite accurate method as long as the paths involved are all Rayleigh distributed. However, in LOS conditions, the strongest path is typically very dominant and has a Ricean distribution (hence it can be fading very weakly or hardly at all). Using a level crossing Doppler estimator in such a situation would result in high relative velocity situations not being detected and the AFC remaining in low speed mode.
In one of its variants, the argument crossing algorithm counts the number of times the complex channel estimate crosses any of the imaginary or real axes, and converts the number of registered axes crossings into a Doppler estimate.
The argument crossing algorithm assumes that the phase variations become faster the higher the relative velocity is, and hence the number of crossings per time unit should correspond to the relative velocity. This is also a quite accurate method as long as the paths involved are all Rayleigh fading. In LOS conditions, however, the strongest path typically experiences a rotation due to Doppler shift, and this rotation typically dominates over the random phase variations. Then the argument crossing Doppler estimator will mainly register the rotation due to changes in the Doppler shift. This creates a severe risk of the relative velocity being underestimated, which may result in, for example, the AFC remaining in low speed mode when it should be switching to high speed mode.
Since none of the conventional Doppler estimation algorithms will detect high speed situations in a LOS environment, their use in the Doppler estimator 209 could keep the AFC 205 in low speed mode when it should be in high speed mode, thereby creating a high risk of AFC wrap-around in these situations.
In addition to the above application, Doppler estimation is also used in wireless communication devices (e.g., a UE) to define other operations (e.g., filter constants) for such things as channel estimation, SIR estimation, and the like.
Therefore, there is a need for methods and apparatuses that can detect high speed movement in LOS situations.